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Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

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Assessment of Hepatic Fibrosis Through Radiomics Models Utilizing Diffusion-Based Virtual Magnetic Resonance

Hua-Shan Liu^1,2, Chia-Bang Chen³, Kuan-Lin Wu³

¹School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan.

Journal of Computer Assisted Tomography

|March 10, 2026

Summary

Radiomics analysis of diffusion MRI-based elastography effectively identifies liver fibrosis stages. This non-invasive approach using shifted apparent diffusion coefficient (sADC) and virtual MR elastography (vMRE) shows promise for clinical use.

Keywords:

diffusion-weighted imaging liver fibrosis magnetic resonance elastography radiomics

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Area of Science:

Radiology
Medical Imaging
Quantitative MRI

Background:

Liver fibrosis staging is crucial for patient management.
Histopathology is the gold standard but invasive.
Non-invasive imaging biomarkers are needed.

Purpose of the Study:

To evaluate radiomic features from diffusion MRI-based elastography for liver fibrosis staging.
To compare classification performance with traditional methods.

Main Methods:

151 patients with liver fibrosis underwent diffusion-weighted imaging (DWI).
Shifted apparent diffusion coefficient (sADC) and virtual MR elastography (vMRE) were calculated.
Radiomic features were extracted and analyzed using a support vector machine.

Main Results:

Radiomics models using sADC and vMRE outperformed models with clinical data.
The sADC + vMRE radiomics model achieved AUCs of 0.90 and 0.91 for significant and advanced fibrosis.
Radiomics scores correlated significantly with histopathologic fibrosis stage.

Conclusions:

Radiomics applied to sADC and vMRE effectively identifies liver fibrosis stages.
This technique offers complementary information to histopathology.